Offline and online identification of hidden semi-Markov models

  • Authors:

  • M. Azimi;P. Nasiopoulos;R.K. Ward

  • Affiliations:

  • Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada;-;-

  • Venue:
  • IEEE Transactions on Signal Processing - Part I
  • Year:
  • 2005

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Abstract

We present a new signal model for hidden semi-Markov models (HSMMs). Instead of constant transition probabilities used in existing models, we use state-duration-dependant transition probabilities. We show that our modeling approach leads to easy and efficient implementation of parameter identification algorithms. Then, we present a variant of the EM algorithm and an adaptive algorithm for parameter identification of HSMMs in the offline and online cases, respectively.